2. Academic Validation
  3. AI-generated MLH1 small binder improves prime editing efficiency

AI-generated MLH1 small binder improves prime editing efficiency

  • Cell. 2025 Aug 1:S0092-8674(25)00799-8. doi: 10.1016/j.cell.2025.07.010.
Ju-Chan Park 1,  Heesoo Uhm 1,  Yong-Woo Kim 2,  Ye Eun Oh 2,  Jang Hyeon Lee 3,  Jiyun Yang 3,  Kyoungmi Kim 3,  Sangsu Bae 4
Affiliations

Affiliations

  • 1 Genomic Medicine Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
  • 2 Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
  • 3 Department of Physiology, Korea University College of Medicine, Seoul, Republic of Korea; Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea.
  • 4 Genomic Medicine Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: sbae7@snu.ac.kr.
PMID: 40769155 DOI: 10.1016/j.cell.2025.07.010
Abstract

The prime editing (PE) system consists of a Cas9 nickase fused to a Reverse Transcriptase, which introduces precise edits into the target genomic region guided by a PE guide RNA. However, PE efficiency is limited by mismatch repair. To overcome this limitation, transient expression of a dominant-negative MLH1 (MLH1dn) has been used to inhibit key components of mismatch repair. Here, we designed a de novo MLH1 small binder (MLH1-SB) that binds to the dimeric interface of MLH1 and PMS2 using RFdiffusion and AlphaFold 3. The compact size of MLH1-SB enabled its integration into existing PE architectures via 2A systems, creating a PE-SB platform. The PE7-SB2 system significantly improved PE efficiency, achieving an 18.8-fold increase over PEmax and a 2.5-fold increase over PE7 in HeLa cells, as well as a 3.4-fold increase over PE7 in mice. This study highlights the potential of generative AI in advancing genome editing technology.

Keywords

AI-generated de novo protein; AlphaFold 3; RFdiffusion; artificial intelligence; genome editing; mismatch repair; prime editing.

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